The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Detection algorithms for hyperspectral imaging applications
Abstract
We introduce key concepts and issues including the effects of atmospheric propagation upon the data, spectral variability, mixed pixels, and the distinction between classification and detection algorithms. Detection algorithms for full pixel targets are developed using the likelihood ratio approach. Subpixel target detection, which is more challenging due to background interference, is pursued using both statistical and subspace models for the description of spectral variability. Finally, we provide some results which illustrate the performance of some detection algorithms using real hyperspectral imaging (HSI) data. Furthermore, we illustrate the potential deviation of HSI data from normality and point to some distributions that may serve in the development of algorithms with better or more robust performance. We therefore focus on detection algorithms that assume multivariate normal distribution models for HSI data.
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Cites
Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution
Reed, I. S., & Yu, X. (1990) · IEEE Transactions on Acoustics, Speech, and Signal Processing · doi:10.1109/29.60107 · Cited by 1,867
Cited by
Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches
Bioucas-Dias, J. M., Plaza, A., Dobigeon, N., Parente, M., Du, Q., Gader, P., & Chanussot, J. (2012) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2012.2194696 · Cited by 2,836
Hyperspectral remote sensing data analysis and future challenges
Bioucas-Dias, J. M., Plaza, A., Camps-Valls, G., Scheunders, P., Nasrabadi, N., & Chanussot, J. (2013) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2013.2244672 · Cited by 2,223
Collaborative representation for hyperspectral anomaly detection
Li, W., & Du, Q. (2015) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2014.2343955 · Cited by 758
Anomaly Detection in Hyperspectral Images Based on Low-Rank and Sparse Representation
Xu, Y., Wu, Z., Li, J., Plaza, A., & Wei, Z. (2016) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2015.2493201 · Cited by 602
Hyperspectral Target Detection : An Overview of Current and Future Challenges
Nasrabadi, N. M. (2014) · IEEE Signal Processing Magazine · doi:10.1109/msp.2013.2278992 · Cited by 580
Metadata
- Authors
- Manolakis, D., & Shaw, G.
- Venue
- IEEE Signal Processing Magazine
- Published
- 2002
- Volume
- 19
- Issue
- 1
- Pages
- 29-43
- ISSN
- 1053-5888 (ISSN-L)
- OpenAlex
- W2067782748
- Cited by
- 1,116
- References
- 1
- FWCI
- 69.91
- Type
- journal article
- Language
- en
- Open access
- closed
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